System and method for closed-loop patient-adaptive hemodynamic management

ABSTRACT

A system and method for patient-adaptive hemodynamic management is described. One embodiment includes a system for hemodynamic management including transfusion, volume resuscitation with intravenous fluids, and medications, utilizing monitored hemodynamic parameters including the described dynamic predictors of fluid responsiveness, and including an intelligent algorithm capable of adaptation of the function of the device to specific patients.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of co-pending U.S.Provisional Application Ser. No. 61/432,081, filed on Jan. 12, 2011,entitled “INTELLIGENT CLOSED-LOOP, PATIENT-ADAPTIVE HEMODYNAMICMANAGEMENT SYSTEM,” which is incorporated herein by reference. Thisapplication is related to co-pending U.S. patent application Ser. No.______, filed on the same date as the present application, entitled“SYSTEM AND METHOD FOR CLOSED-LOOP PATIENT-ADAPTIVE HEMODYNAMICMANAGEMENT” (temporarily referenced by Attorney Docket No. 1279-536), toco-pending U.S. patent application Ser. No. ______, filed on the samedate as the present application, entitled “SYSTEM AND METHOD FORCLOSED-LOOP PATIENT-ADAPTIVE HEMODYNAMIC MANAGEMENT” (temporarilyreferenced by Attorney Docket No. 564), and to co-pending U.S. patentapplication Ser. No. ______, filed on the same date as the presentapplication, entitled “SYSTEM AND METHOD FOR CLOSED-LOOPPATIENT-ADAPTIVE HEMODYNAMIC MANAGEMENT” (temporarily referenced byAttorney Docket No. 1279-566). The contents of each of theseapplications are hereby incorporated by reference herein in theirentirety for all purposes.

FIELD

The disclosure relates to an apparatus and method for hemodynamicmanagement capable of facilitating fluid administration, transfusion ofblood products, and administration of blood pressure supportingmedications.

BACKGROUND

In the past, fluid resuscitation has been approached directly byclinicians using vital signs, or clinical guidelines regarding urineoutput, for example. Previous automated systems have been tried usingeither urine output or other physiologic parameters like blood pressureor heart rate, all of which have been unable to accurately predict fluidresponsiveness. In some cases trial-and-error was the best optionavailable.

Although present devices are functional, they are not sufficientlyaccurate or otherwise satisfactory.

SUMMARY

In one aspect the disclosure describes a method that incorporates thedynamic predictors of fluid responsiveness (“fluid predictors” i.e.pulse-pressure variation or PPV, stroke volume variation or SVV,parameters derived from the plethysmograph waveform, etc) which havebeen shown to reliably predict a response to fluid bolus in specificconditions. This allows directed fluid management with the goal ofoptimizing cardiac output.

In another aspect, a method is provided that incorporates the dynamicpredictors of fluid (“fluid predictors”) responsiveness in conjunctionwith a patient-adaptive monitoring system that adjusts output based onprevious responses represents a substantial improvement over previouslyproposed automated systems.

Using a combination of the change in the fluid predictive parameters andthe change in cardiac output in response to a bolus allows a veryspecific measurement of the bias present in a particular patient at aparticular time and allows for patient-adaptive responses to beeffected. Additionally, using a variety of available vital signs andcardiac output information allows for appropriate administration ofblood-pressure supporting pharmacologic agents.

In another aspect, a method and system is provided comprising a set ofprocesses and a device based on those used to administer IV fluids,blood and medications to patients autonomously.

In another aspect, a computer-implemented method for controlling fluidadministration is provided. The method includes receiving inputinformation relating to one or more physiologic processes of a patient,determining, based at least in part upon the input information, asubgroup of bolus log entries associated with a current state of thepatient, and adjusting, using a processor, administration of fluid tothe patient based at least in part upon log data included within thesubgroup of bolus log entries.

In yet another aspect, a device is provided that includes one or moreprocessors; and a memory operatively coupled to the one or moreprocessors. The memory stores signals which, when executed by the one ormore processors, cause the one or more processors to receive inputinformation relating to one or more physiologic processes of thepatient, and to determine, based at least in part upon the inputinformation, a subgroup of bolus log entries associated with a currentstate of the patient. The signals further cause the one or moreprocessors to adjust administration of fluid to the patient based atleast in part upon log data included within the subgroup of bolus logentries.

In a further aspect, an infusion system includes a pump apparatusconfigured to control delivery of fluid to a patient, and a controllerthat receives input information relating to one or more physiologicprocesses of the patient. The controller then determines a subgroup ofbolus log entries associated with a current state of the patient basedat least in part upon the input information, and adjusts administrationof fluid to the patient based at least in part upon log data includedwithin the subgroup of bolus log entries.

In still another aspect, a computer-implemented method for facilitatingthe administration of fluid to a patient is provided. Thecomputer-implemented method includes determining a first effect on aphysiologic parameter of the patient associated with administration of afirst fluid bolus to the patient, and then storing, using a processor,first information relating to the first effect in a first bolus logentry. The method further includes determining a second effect on thephysiologic parameter of the patient associated with administration of asecond fluid bolus to the patient, and then storing, using a processor,second information relating to the second effect in a second bolus logentry. The method further includes generating, using the processor, afluid administration signal based at least in part upon at least one ofthe first bolus log entry and the second bolus log entry.

In another aspect, a device is provided that includes one or moreprocessors, and a memory operatively coupled to the one or moreprocessors, the memory storing program code which, when executed by theone or more processors, determines a first effect on a physiologicparameter of the patient associated with administration of a first fluidbolus to the patient, and then stores first information relating to thefirst effect in a first bolus log entry within device storage. Thememory further stores program code which determines a second effect onthe physiologic parameter of the patient associated with administrationof a second fluid bolus to the patient, stores second informationrelating to the second effect in a second bolus log entry within thedevice storage, and generates a fluid administration signal based uponat least one of the first bolus log entry and the second bolus logentry.

In yet another aspect, an infusion system includes a pump apparatusconfigured to control delivery of fluid to a patient, and a controller.The controller of this aspect determines a first effect on a physiologicparameter of the patient associated with administration of a first fluidbolus to the patient, and stores first information relating to the firsteffect in a first bolus log entry within device storage. The controllerthen determines a second effect on the physiologic parameter of thepatient associated with administration of a second fluid bolus to thepatient, and stores second information relating to the second effect ina second bolus log entry within the device storage. The controllerprovides a fluid administration signal to the pump apparatus based uponat least one of the first bolus log entry and the second bolus logentry.

In still another aspect, a computer-implemented method for providingclinical decision support relating to the administration of fluid to apatient is provided. The method includes receiving input informationrelating to one or more physiologic processes of a patient, determining,based at least in part upon the input information, a subgroup of boluslog entries associated with a current state of the patient, andproviding, using the processor, a fluid administration recommendationbased at least in part upon log data included within the subgroup ofbolus log entries.

In a further aspect, a computer-implemented method for providingclinical decision support relating to the administration of fluid to apatient is provided. The method of this aspect includes determining afirst effect on a physiologic parameter of the patient associated withadministration of a first fluid bolus to the patient, storing, using aprocessor, first information relating to the first effect in a firstbolus log entry, determining a second effect on the physiologicparameter of the patient associated with administration of a secondfluid bolus to the patient, storing, using a processor, secondinformation relating to the second effect in a second bolus log entry,and providing, using the processor, a fluid administrationrecommendation based at least in part upon at least one of the firstbolus log entry and the second bolus log entry.

In yet another aspect, a computer-implemented method for providingclinical decision support relating to the administration of fluid to apatient is provided. The method of this aspect includes receiving boluslog information relating to one or more effects on a state of thepatient associated with prior administration of fluid to the patient,then determining, using a processor and based upon the bolus loginformation, a predicted change in a physiologic parameter of thepatient in response to the administration of a fluid bolus to thepatient. The method further includes providing, using the processor, afluid administration recommendation based upon the predicted change.

In another aspect, a computer-implemented method for facilitating theadministration of fluid to a patient is provided. The method of thisaspect includes receiving bolus log information relating to one or moreeffects on a state of the patient associated with prior administrationof fluid to the patient, and determining, using a processor and basedupon the bolus log information, a predicted change in a physiologicparameter of the patient in response to the administration of a fluidbolus to the patient. The method further includes generating, using theprocessor, a fluid administration signal based upon the predictedchange.

In a further aspect, a system is provided that includes: 1) a means ofcalculating the expected increase in cardiac output in the generalpopulation in response to a fluid bolus given a specific set ofphysiologic parameters. This calculation is based on previouslypublished and unpublished data; 2) a means of calculating the expectedincrease in cardiac output in a specific patient given a specific set ofphysiologic parameters and data collected from previous fluidadministrations; 3) a means of calculating bias, artifact, and error inthe response to fluid, in part based on the difference between thechange in actual cardiac output and the change in the dynamic predictorand the predictable relationship between the two; 4) calculations fordetermining whether or not blood pressure supporting medications areindicated, and if so how much; and 5) calculations for determiningwhether or not blood product administration is indicated, and if so howmuch.

In yet a further aspect, a clinical device is provided that is capableof administering fluids, blood products, and medications based on thealgorithms above, monitoring the patient response, and displaying themonitored information in a specific and novel way to the practitioner.One purpose of this device is to automate and standardize administrationof intravenous fluids, blood products, and blood-pressure supportingmedications to assist clinicians with the eventual goal of improvingoutcomes.

In an additional aspect, an apparatus for hemodynamic and cardiac outputmanagement in a patient is provided, comprising a computer readablestorage medium storing instructions to perform a method of managinghemodynamic and cardiac output in a patient, the method comprising thesteps of determining, given a set of available physiologic data obtainedfrom patient monitoring devices, which parameters to use and in whatcombinations to predict fluid responsiveness; and determining theexpected increase in cardiac output in the general population inresponse to a fluid bolus of specific size given the chosen set ofphysiologic parameters; and determining patient-specific bias in theresponse to said fluid based on the patient's prior responses to fluidadministration.

The apparatus may further comprise detecting and filtering artifact inthe monitored data; and detecting and filtering error in the patientbias using the strong relationship between the predictive parameters andtheir response to fluid administration, especially to detect ongoingbleeding or fluid shifts which might influence bias.

The apparatus may also comprise dynamically adapting to specificpatients using the known biases in conjunction with associatedphysiologic parameters, their means and standard deviations inrelationship to one another, and observed responses to previousinterventions by the apparatus; determining whether blood-pressuresupporting medications are indicated and if so administer them, againmonitoring responses and adapting to the patient, and determiningwhether blood product administration is indicated and if so administerthem.

The apparatus may be further configured such that adaptation andlearning is further enhanced by data shared between devices over time toimprove population expectations and the processes of the apparatus.

The apparatus may be further configured such that the adapting processis concerned not only with adjustments to the fluid administrationvolume and threshold and to the medication administration dose andthreshold, but also with automatic adjustments to the weight of eachmeasured parameter in decision-making by the apparatus.

The apparatus may further comprise pumps capable of injecting fluid ormedication into said patient.

The apparatus may be further configured to operate, in real time andwithout operator interaction, to automatically adjust the anticipatedresponse to a new bolus based on the bias information in astate-dependent fashion.

In yet another aspect, a method for hemodynamic and cardiac outputmanagement in a patient is provided comprising: obtaining physiologicdata from a patient, injecting fluid, medications, or blood productsinto a patient as indicated, measuring the results of said interventionsin said patient, adapting future interventions based on data collectedfrom previous interventions said fluid to said patient.

Those skilled in the art can readily recognize that numerous variationsand substitutions may be made in the invention, its use and itsconfiguration to achieve substantially the same results as achieved bythe embodiments described herein. Accordingly, there is no intention tolimit the invention to the disclosed exemplary forms. Many variations,modifications and alternative constructions fall within the scope andspirit of the disclosed invention as expressed in the claims.

As previously stated, the above-described embodiments andimplementations are for illustration purposes only. Numerous otherembodiments, implementations, and details of the invention are easilyrecognized by those of skill in the art from the following descriptionsand claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Various objects and advantages and a more complete understanding of thepresent invention are apparent and more readily appreciated by referenceto the following Detailed Description and to the appended claims whentaken in conjunction with the accompanying Drawings wherein:

FIG. 1A illustrates details of an exemplary patient-adaptive hemodynamicmanagement system in accordance with the disclosure;

FIG. 1B illustrates details of an embodiment of a control device thatcan be used, for example, in the system of FIG. 1A;

FIG. 2 illustrates details of an embodiment of a vitals manager and logcomponent that can be used, for example, in the control device of FIG.1B;

FIG. 3 illustrates details of an embodiment of a population basedpredictor component that can be used, for example, in the control deviceof FIG. 1B;

FIG. 4 illustrates details of an embodiment of a fluid bolus logcomponent that can be used, for example, in the control device of FIG.1B;

FIG. 5 illustrates details of an embodiment of a log predictor componentand an embodiment of a history analysis component that can be used, forexample, in the control device of FIG. 1B;

FIG. 6 illustrates details of an embodiment of a prediction engine thatcan be used, for example, in the control device of FIG. 1B;

FIG. 7 illustrates details of an embodiment of an intervention decisioncomponent that can be used, for example, in the control device of FIG.1B;

FIG. 8 illustrates details of an embodiment of a pump manager componentthat can be used, for example, in the control device of FIG. 1B;

FIG. 9 illustrates details of another exemplary patient-adaptivehemodynamic management system in accordance with the disclosure;

FIG. 10 illustrates information flow between various components of thepatient-adaptive hemodynamic management system of FIG. 1A;

FIG. 11 is a flowchart depicting an exemplary method of providingpatient-adaptive hemodynamic management in accordance with thedisclosure;

FIG. 12 is a flowchart depicting an exemplary method of providingpatient-adaptive hemodynamic management including a user intervention inaccordance with the disclosure;

FIG. 13 is a flowchart depicting another exemplary method of providingpatient-adaptive hemodynamic management in accordance with thedisclosure;

FIG. 14 is a flowchart depicting another exemplary method of providingpatient-adaptive hemodynamic management in accordance with thedisclosure;

FIG. 15 is a flowchart depicting another exemplary method of providingpatient-adaptive hemodynamic management in accordance with thedisclosure;

FIG. 16 is a flowchart depicting another exemplary method of providingpatient-adaptive hemodynamic management in accordance with thedisclosure;

FIG. 17 is a flowchart depicting another exemplary method of providingpatient-adaptive hemodynamic management in accordance with thedisclosure;

FIG. 18 is a flowchart depicting another exemplary method of providingpatient-adaptive hemodynamic management in accordance with thedisclosure;

FIG. 19 is a flowchart depicting another exemplary method of providingpatient-adaptive hemodynamic management in accordance with thedisclosure;

FIG. 20 is a flowchart depicting another exemplary method of providingpatient-adaptive hemodynamic management in accordance with thedisclosure;

FIG. 21 is a flowchart depicting another exemplary method of providingpatient-adaptive hemodynamic management in accordance with thedisclosure;

FIG. 22 is a flowchart depicting another exemplary method of providingpatient-adaptive hemodynamic management in accordance with thedisclosure;

FIG. 23 is a flowchart depicting yet another exemplary method ofproviding patient-adaptive hemodynamic management in accordance with thedisclosure;

FIG. 24 is a flowchart depicting another exemplary method of providingpatient-adaptive hemodynamic management including a clinicalintervention in accordance with the disclosure;

FIG. 25 is a flowchart depicting another exemplary method of providingpatient-adaptive hemodynamic management including a clinicalintervention in accordance with the disclosure;

FIG. 26 is a flowchart depicting yet another exemplary method ofproviding patient-adaptive hemodynamic management including a clinicalintervention in accordance with the disclosure; and

FIG. 27 is a summary of results from initial studies using themethodology of the control device in simulations.

In the appended figures, similar components and/or features may have thesame reference label. Further, various components of the same type maybe distinguished by following the reference label by a dash and a secondlabel that distinguishes among the similar components. If only the firstreference label is used in the specification, the description isapplicable to any one of the similar components having the same firstreference label irrespective of the second reference label.

DETAILED DESCRIPTION

An intelligent, closed-loop, patient-adaptive hemodynamic managementsystem and method are provided to monitor hemodynamic parameters,including those predictive of fluid responsiveness, the system beingcapable of fluid administration, transfusion of blood products, andadministration of blood pressure supporting medications based on thoseparameters in conjunction with patient-adaptive algorithms as well aspooled patient data, and displaying the monitored parameters in a wayeasily interpreted by practitioners.

The system and method provided are based on hemodynamic monitoringincluding dynamic parameters of fluid responsiveness (‘fluidpredictors’) derived from arterial pressure waveform, plethysmographwave form, thoracic ultrasound, bioimpedance, bioreactance or EKGwaveform, for example.

This control device and process are designed to utilize, among otherphysiologic data, the dynamic predictors of fluid responsiveness (‘fluidpredictors’). As there are several described parameters that meet thesecriteria such as, for example, pulse-pressure variation (PPV), strokevolume variation (SVV), plethysmograph variability, and EKG waveformcharacteristics, the description will simply refer to the group as the“Fluid Predictors” or FP. This term should be taken to mean any of thedescribed predictors of fluid responsiveness.

Additionally, all of the physical values and constants in the disclosureare subject to change based on results of ongoing studies as the deviceand algorithm are refined; values contained herein should be taken to beexemplary at the time of this writing.

Other terms and abbreviations used herein include:

-   -   CO-Cardiac output    -   Patient or Subject—the “patient” or “subject” is the organism        being monitored by and managed by the system. In one embodiment,        the patient is a human being. In another embodiment, the patient        may be any mammal, reptile, amphibian, or bird of sufficient        size to make intravascular resuscitation an appropriate strategy        for management of cardiac output and oxygen delivery.    -   Vital Signs or Vitals—Any statistical measure of a physiologic        process taking place in a patient—including waveforms derived        from physiologic processes. Vitals can include, for example:        -   Heart Rate (HR)—the number of ventricular contractions per            minute        -   Stroke Volume (SV)—the volume of blood ejected by the left            ventricle during contraction in milliliters.        -   Systolic Blood Pressure (SBP)—the highest blood pressure            felt in the systemic arterial vascular tree during a cardiac            cycle.        -   Diastolic Blood Pressure (DBP)—the lowest blood pressure            felt in the systemic arterial vascular tree during a cardiac            cycle.        -   Mean Arterial Pressure (MAP)—the average blood pressure in            the systemic arterial system over one or more cardiac            cycles, typically calculated as ((SBP+DBP+DBP)/3).        -   Systemic Vascular Resistance (SVR)—An index of arteriolar            constriction throughout the body measured in dyn·s/cm-5        -   Cardiac Output—the total volume of blood ejected by the left            ventricle over one minute        -   Dynamic Predictor (DP)—one or more measures of preload            dependence derived from the arterial pressure waveform,            plethysmograph waveform, EKG waveform, thoracic ultrasound,            bioimpedance, bioreactance, and including specific maneuvers            such as passive leg raising and tele-expiratory pause. As            there are several described parameters that meet this            criteria (pulse-pressure variation, stroke volume variation,            plethysmograph variability, EKG waveform variation in lead            II, and more) the phrase Dynamic Predictor will be            understood to represent any one or more of the measures in            this group. The term fluid predictor may be used            interchangeably with the term Dynamic Predictor in the            present disclosure.    -   Intravenous fluid (IV Fluid)—any fluid intended for        administration intravenously to a monitored subject for the        purpose of intravascular volume expansion or increasing oxygen        delivery. IV Fluid would therefore include, but not be limited        to: crystalloid solutions like Lactated Ringer's Solution,        Normal Saline, Dextrose Solutions, Plasmalyte, and in general        balanced salt solutions and sugar solutions; colloidal solutions        like albumins, starches, and similar; and blood products and        blood analogs like whole blood, platelets, fresh frozen plasma,        cryoprecipitate, packed red blood cells, salvaged cellular        solutions, or any substitutes meant to mimic or replace these        products.    -   Fluid bolus—an administration of a specific volume of IV Fluid        over a discrete timespan.    -   Supervisor (system supervisor, user)—a human user who monitors        system operation, and who may, in one embodiment, accept or        reject system recommendations before the system acts on those        recommendations, and who may, in another embodiment, at any        time, override system operation in favor of a user directed        action.    -   “Efficacy” of a Fluid Bolus—the degree to which the        intravascular administration of said fluid increases the cardiac        output; or the degree to which the intravascular administration        of said fluid improves the delivery of oxygen to the tissues,        for example.    -   Prediction—the calculated percent increase in cardiac output        that a fluid bolus would be expected to cause in the patient.    -   Vasoactive Medications—medications controlled by the system that        could include those intended to manipulate blood pressure and        cardiac output such as, for example, ephedrine, phenylephrine,        norepinephrine, epinephrine (adrenaline), dopamine dobutamine,        milrinone, dopexamine, nitroglycerine, nitroprusside, and other        vasopressors, inotropes, and vasodilators.

With reference to FIG. 1A, a patient-adaptive hemodynamic managementsystem 10 includes a control device 100 coupled via electronicinterfaces (not shown) to a first fluid pump 109-1 and a second fluidpump 109-2. The control device 100 includes a display 120. The display120 includes a display screen, electronics and interfaces coupled to thefirst and second fluid pumps 109-1 and 109-2. The first fluid pump 109-1is coupled to fluid sources such as, for example, IV bags 125 containingany of various IV fluids. The second fluid pump 109-2 is coupled tomedications such as, for example, medication vials 130 containing fluidmedications. The IV bags 125 and medication vials 130 are fluidicallycoupled to IV tubing 135 which is coupled to a patient 110 such that theIV fluids and medication can be administered to the patient anddynamically controlled by the control device 100.

The control device 100 includes one or more processing units (notshown), and one or more computer readable storage medium (not shown).The processing units may be implemented within one or more applicationspecific integrated circuits (ASICs), digital signal processors (DSPs),digital signal processing devices (DSPDs), programmable logic devices(PLDs), field programmable gate arrays (FPGAs), processors, controllers,micro-controllers, microprocessors, other electronic units designed toperform the functions described above, and/or a combination thereof. Thecomputer readable storage medium may include one or more memories forstoring data, including read only memory (ROM), random access memory(RAM), magnetic RAM, core memory, magnetic disk storage mediums, opticalstorage mediums, flash memory devices and/or other machine readablemediums for storing information. The computer readable storage mediummay be embodied in one or more portable or fixed storage devices,optical storage devices, wireless channels, and/or various other storagemediums capable of storing that contain or carry instruction(s) and/ordata.

The control device 100 is coupled to the fluid pumps 109 via interfaceports. The interface ports can include one or more of a standard USBport and a standard serial port. The USB connector can also be used toimport patient data in real-time from another source such as a patientmonitor (not shown). Optionally, an external component may be connectedto the control device 100 which will allow for direct monitoring ofpatient vital signs by the control device 100. Finally, the USB/Serialports will allow data to be transferred to and from the control device100 for sharing data with other networked equipment (not shown), and forreceiving firmware upgrades. Additional ports may be added (forinterface with electronic records systems, for example).

In some embodiments, the display 120 provides a touch-screen interfacefor monitoring vital signs of the patient and for entering patient dataand user preferences into the control device 100.

The first fluid pump 109-1 can be integrated in the same housing as thecontrol device 100 or can be an external pump. In either case, the firstfluid pump 109-1 can regulate and drive the flow of fluid from the IVbags 125 to the patient, as well as select which fluid to use from whichIV bag 125. The IV fluids can include one or more of crystalloids,colloids, or blood products as well as other fluids. The second fluidpumps 109-2 can be integrated with or external to the control device 100and can be syringe pump systems for use with multiple medication vials130. One or more pumps may be included but are not required for standarduse. The fluid pumps 109 can also contain an air detector to hold theinfusion in the event air is detected in the tubing. The control device100 can be coupled to commercially available pumps to control those.

The IV tubing 135 is typically disposable and is coupled with thecontrol device after flushing and prior to use, as for any standard IVfluid pump. The IV tubing 135 is depicted as a single tube at thepatient 110 and multiple tubes at the IV bags 25 and the medicationvials 130. However, multiple IV tubes 135 can be connected to thepatient 110. The disposable IV tubing 135 is sterile IV tubing where anew disposable tube set is used for each patient to maintain sterility.One end of IV tubing 135 can have standard IV bag taps for use withstandard IV solutions, colloids, and blood products. The opposite end ofthe IV tubing 135 can be a male luer lock for connection to standard IVtubing sets and claves. The disposable IV tubing can also have sideports distal to the main fluid pump which the medication syringes fromthe medication vials 130 can be attached to.

FIG. 1B illustrates the system 10 including details of an embodiment ofthe control device 100. Each of the components of the control device 100shown in FIG. 1B are described in detail in the figures and sectionsthat follow.

Referring to FIG. 1B, the surgical patient 110 is monitored by one ormore clinical monitors 115, which are coupled with the control device100. The clinical monitors 115 can be integrated with or separate fromthe control device 100. The vitals measured by the clinical monitors 115are communicated in some fashion to a vitals manager and log component101, which, in some embodiments, filters the incoming data for noise andvalidity and then maintains an ongoing record of the validated data in avitals log.

Data from the vitals manager component 101 is passed on request to thepopulation based predictor component 102. The population based predictorcomponent 102 is responsible for making predictions about the likelyefficacy of a certain fluid bolus by comparing the received vitals ofthe patient 110 to mean responses obtained from a previous population ofpatients with similar vitals in response to the certain fluid bolus.

Data from the vitals manager component 101 is also passed, on request insome embodiments, to a fluid bolus log component 103. The data istypically passed or requested when a fluid bolus is initiated orterminated, so that the vitals and calculations based on them may beincluded in the fluid bolus log with the appropriate bolus. The fluidbolus log component 103 also receives inputs from a pump managercomponent 108 (e.g., when fluid boluses are initiated or terminated)including the relevant details of the bolus being administered.

Information from the fluid bolus log component 103 is output to a logpredictor component 104. The log predictor component 104 is configuredto analyze the known history of the current patient 110, and, takinginto account the vitals and sub-analyses based on the vitals, predictthe current efficacy of a fluid bolus.

The log predictor component 104 is also configured, after determiningthe appropriate segments of the Log that are applicable to the patient110 in the current state and finishing its predictive analysis, to passthe same segments it used for analysis on to a history analysiscomponent 105. The history analysis component 105 then furthercharacterizes these segments to examine the mean historical error ofboth the population based predictor component 102 and the log predictorcomponent 104 by determining, for example, the standard deviation ofthese errors in order to make corrections to current predictions.

A prediction engine 106 takes the outputs from the population basedpredictor component 102, the log predictor component 104, and thehistory analysis component 105, and uses these outputs to formulate acombined prediction in cardiac output for the current state of thepatient 110. This combined prediction is passed on to an interventiondecision component 107 which takes the predicted change in cardiacoutput and determines the appropriate course of action for the controldevice 100. This action may be modified by user specifications in thiscomponent.

Finally, the action dictated by the intervention decision component 107is communicated to the pump manager component 108, which is responsiblefor communication of the action with a supervisor for verification (ifnecessary) and the actual hardware level control and monitoring of thefluid pump(s) 109.

The fluid pump(s) 109, in one embodiment, are externally controlledfluid pumps which contain their own command interface, alarm system, andconfiguration. The control of the fluid pumps 109 can be achieved overserial, network, wireless, Bluetooth, or other electronic protocols. Thespecific design of the fluid pumps 109 is not essential beyond thecharacteristic that they are able to variably control the rate ofadministration of IV Fluid and/or medication 113 into the patient 110.There may be one, two, or more than two physical fluid pumps 109,depending on the embodiment.

In another embodiment, the fluid pump(s) 109 are an integrated componentof the control device 100. In this embodiment, the fluid pumps 109 mayor may not include alarm and control configurations. If the fluid pumps109 do not include alarms and controls, the alarms and controls for thepumps can be included in the control device 100. A user interface (notshown) of the control device 100 can be used to affect some aspects ofthe fluid pumps 109. There may be one, two, or more physical pumps 109in this embodiment.

In yet another embodiment, the fluid pump(s) 109 are the embodiment of acontrol device 100 with the entirety of the system built into thehardware and electronic control scheme of the fluid pump 109. This pumpmay include one, two, or more individual fluid set channels for control.

As expected, the fluid pump(s) 109, deliver IV fluid and/or medications113 to the patient 110 at the rate and times dictated by the method ofthe claim, in some cases as approved by the supervisor.

An SV/CO monitor 111 is another component that keeps track of the strokevolume and cardiac output over time. The SV/CO monitor 111 canindependently provide information about the current stroke volume andcardiac output compared to the average and maximum SV/CO and thisinformation can be used by the intervention decision component 107either alone or in conjunction with the other components to determinewhether or not to provide fluid to the patient.

FIG. 2 illustrates details of an embodiment of a vitals managercomponent 101 that can be used, for example, in the management system 10of FIGS. 1A and 1B. The vitals manager component 101 is responsible forthe acceptance and processing of new vital signs received from clinicalmonitors 201. In one embodiment, clinical monitors 115 are includedwithin the control device 100 and are connected to the patient 110 andsimply pass the collected data along to the vitals manager component 101internally. In another embodiment, external or third-party clinicalmonitors 115 are connected to the patient and the data from the clinicalmonitors 115 is sent to the control device 100 through data interfacesusing communications protocols, including but not limited to directserial connections/RS232, TCP/IP or other network protocols includingwireless and Bluetooth, USB, and direct analog signals. This data, in aone embodiment, could be expected to arrive about every second, but maybe as often as every 1/1000th of a second or less in the case ofdigitized waveforms or even continuously in analog signals, and may beas infrequent as once a minute in low-performance embodiments.

Regardless of the original source of the information, the vital signsdata 112 is received by the vitals manager component 101 and first passthrough an artifact and noise filter component 206, referred to fromherein as the artifact filtration component 206. The artifact filtrationcomponent 206 compares the new vital signs data 112 both to itself (forinternal consistency) and to previously received data (for consistencywith regards to trends and time), and to general rules about limits onspecific parameters. If the new vital signs data 112 is found to satisfythe requirements of the noise filters (e.g., within threshold limitsand/or within threshold changes over a threshold time) it is deemedvalid and passed on to the Vitals Log table 202. If not, it is rejectedas being a temporary artifact and the vital signs data 112 discarded.

To accomplish this artifact detection, in one embodiment, a mean valueand standard deviation can be calculated for any parameter inrelationship to any other hemodynamic parameter over time. Thiscalculation is performed for any relevant decision-making variables (forexample, the DP and CO). If the target parameter is outside the standarddeviation for the associated parameters of comparison, it is more likelythat this new measurement is an artifact and will be flagged by thesystem and temporarily ignored.

Over the next few measurements, if the target variable returns to arange expected for the associated variables, the previously detectedartifact value(s) is/are left flagged and are ignored in any analysis orprocessing of the vital signs data 112. However, if the target variableremains outside the expected range for longer than a discrete timespan,or the incoming vital signs data 112 change such that they are now inranges correlating with the vital sign of interest, the flags areremoved and the measurement is assumed to be real.

Additionally, certain parameters measured from the sameequipment/monitors will be expected to reflect artifacts simultaneously.For example, a heart rate, blood pressure, and stroke volume variationfrom an arterial line waveform would be expected to either all showartifact or none if the signal were to become noisy.

Similarly, a parameter monitored from different clinical monitors 115but reporting the same vital sign (for example, a heart rate calculatedfrom the EKG waveform, arterial line waveform, and plethysmographwaveform) would be expected to change in all three measurements. Ifthere is a significant difference between measurements from differentclinical monitors 115, the artifact filtration component 206 candetermine that there is artifact in one or more of the signals and thatone or more of the measurements is likely incorrect.

Referring again to FIG. 2, a Vitals Log table 202, in one embodiment,maintains a complete record of all vital signs data 112 accepted fromthe clinical monitors 115 regarding the patient 110, whether temporallycontiguous or not. Further, should a different control device 100 beused on the same patient 110, the data from a previous control device100 could be transferred to the new vitals log table 202 of the newcontrol device 100, either over a network, through a portable mediadevice like a USB drive or electronic smart card, or via some other formof electronic media. At any time, the vitals log table 202 will servicevitals request inputs 204 for patient data at given times that arereceived, for example, from other components of the management system10. Said vitals request inputs 204 can include both a time point and atime span (e.g., a start time and an end time, or a start time andduration). The time span indicates the period over which the vitals logtable 202 should compile and average (or perform other statisticalanalysis on) the requested data. The time point indicates when thisaverage time frame data should be pulled from the log; this is notnecessarily the current time but may be any time, past or future, overthe entire monitoring period of the patient 110. The response to thisrequest is, in one embodiment, a time averaged vitals pack output 205,which contains the requested average vitals and trend information (seebelow), and is accepted as input at other components of the system.

One adjunct component to the vitals log table 202 is the vitals trendprocessor 203. When new vital signs data 112 is added to the vitals logtable 202, the vitals trend processor 203 will take the new data alongwith any or all portions of the entirety of the preceding data andanalyze, identify and store specific trends. These trends will be storedin the vitals log table 202 along with the new data on acceptance suchthat the trends at this time point are also available immediately forany incoming vitals request input 204. Such trend analysis, in oneembodiment, includes factors like the percent change in heart rate, meanarterial pressure, dynamic predictor, cardiac output, stroke volume, andsystemic vascular resistance over the last two minutes, five minutes,and ten minutes.

FIG. 3 illustrates details of an embodiment of a population basedpredictor component 102 that can be used, for example, in the controldevice 100 of FIG. 1B. The population based predictor component 102receives a vitals pack output (time averaged, for example) 205 suppliedby the vitals manager component 101. The vitals pack output 205 is firstreceived by a vitals processing component 302 and distilled intospecific core measures for lookup in population reference tables 306. Inone embodiment of the system, these measures can include heart rate,stroke volume, systemic vascular resistance, and indications dynamicpredictor(s) being utilized, but many other measures areinterchangeable. For example, as cardiac output is nothing more thanstroke volume multiplied by heart rate, cardiac output could be used inplace of either heart rate or stroke volume with equal efficacy, andmany other such replacements are feasible. The limit or requirement offour parameters necessary; other embodiments may include fewer or more.Conceptually, the purpose of this distillation of vitals pack output 205is to succinctly characterize the patient's overall hemodynamic stateduring the time frame in question in as complete but concise a manner aspossible for comparison with the population data (which has beenpreviously similarly characterized and stored in the populationreference tables 306).

Once the distillation and characterization of the vitals pack output 205is completed by the vitals processing component 302, a reference tableselector component 303 identifies one of a plurality of the populationreference table 306 based on a comparison of (1) patient demographicsand comorbidities input 301 received from the vitals manager component101, and (2) any dynamic predictor(s) available, with similar datastored in association with the population reference tables 306. Thepopulation reference tables 306 are multi-dimensional references thatlink specific patient characterizations (e.g., patient demographics andcomorbidities and dynamic predictors) in specific sub-populations ofpatients, to an expected increase in cardiac output. The patientdemographics and comorbidities input 301 will be used by the populationbased predictor component 102 to determine which population referencetable 306 is the most appropriate reference to use based on the evolvinginformation about how patient diseases and demographic factors influencethe dynamic predictors and cardiac output. In an example embodiment, an80-year old smoker with heart failure would lead to the selection of apopulation reference table 306 that included only elderly smokers withheart failure, while a similar non-smoking patient would lead to adifferent population reference table 306 for non-smokers. The populationreference tables 306 are expected to evolve as significant sub-groupsare identified. Furthermore, all of the population reference tables 306can be identified by not only the patient population represented, but bya particular dynamic predictor or predictors as well. Thus, there willbe different population reference tables 306 for the example 80 year oldsmoker if pulse-pressure variability is available rather thanplethysmograph variability, for example. Finally, more generalpopulation reference tables 306 can also be available such that apatient who does not fit the criteria of a narrow subpopulation can bereferenced against the broader population.

Following the characterization of the patient state with the vitalsprocessing component 302 and the selection of the population referencetable 306 with the reference table selector component 303, a populationlookup and hybridization component 304 will cross-reference the patientcharacterization with the chosen population reference table 306 andidentify the expected increase in cardiac output for the given patientin the current state. This expected increase in cardiac output is, inthe standard embodiment, the amount the cardiac output would be expectedto increase in response to distinct volume of intravenous fluid over adistinct time span. For example, in one embodiment, this could be 500 mlof fluid over 10 minutes, but any volume and timeframe are possible.This predicted increase, along with a measure of the quality of theprediction (based upon the specificity of the population reference table306 and the quality and frequency of the vitals pack output data 205received from the vitals manager component 101) is exported in apopulation prediction output 305 to subsequent components of the controldevice 100.

FIG. 4 illustrates details of an embodiment of a fluid bolus logcomponent 103 that can be used, for example, in the control device 100of FIG. 1B. The purpose of the fluid bolus log component 103 is to trackand store bolus log entries 400 that include information associated witheach distinct volume of intravenous fluid delivered by the controldevice 100 into a patient 110. Each bolus log can include one or more ofa start time, an end time, a patient condition both before and after thebolus, and the impact of the fluid bolus on hemodynamics. Each bolus logentry 400 is initiated with the acceptance of an intervention command415 at a bolus start interface 410. In addition each bolus log entry 400includes a first vitals pack output 205-1 at start time received by thebolus start interface 410 from the vitals manager component 101. Thefirst vitals pack output 205-1 includes information indicative of thevitals of the patient 110 at or prior to the start time of the fluidbolus. The intervention command 415 also includes an end time thatdetermines the timespan over which the fluid is to be delivered. The endtime information of the intervention command 415 is received by a bolusend interface 420. At the end of the bolus timespan, or shortly beforeor after the timespan, the bolus end interface 420 receives a secondvitals pack output 205-2. The second vitals pack output 205-2 includesinformation indicative of the vitals of the patient 110 at or near theend of the fluid bolus.

Upon initiation of a fluid bolus, the fluid bolus log component 103creates a new bolus log entry 400 and assigns a bolus ID number 401 tothe bolus log entry 400. In addition, the intervention command includesa reason for initiation of the bolus. The fluid bolus log component 103stores the reason for initiation of the bolus 402 in the new bolus logentry 400. The first vitals pack output 205-1 is also recorded as apatient vitals at start time 403 in the bolus log entry 400 such thatthe bolus log entry 400 contains a complete record of the patient vitalsat the start of each fluid administration without need to access otherdata structures within the system. In another embodiment, first vitalspack output 205-1 is excluded from this bolus log entry 400 and merelythe start time recorded, with reference made to the vitals log table 202or another similar data store based on the start time should vital signsdata be needed. The new bolus log entry 400, as identified by thedistinct bolus ID 401, is left open while the fluid bolus isadministered.

Following the conclusion of the fluid bolus, a waiting period can beallowed to pass before analyses of the results of the bolus areattempted. In one embodiment, this period is about two minutes, but thiswaiting period could be as little as instantaneous to as long as tenminutes without changing the significance of the post-analysis.Following the determined time span of the bolus, an end command of theintervention command 415 is received at the end bolus interface 420 andthe fluid bolus is terminated. The fluid bolus log component 103 storesthe end bolus time information 404 in the bolus log entry 400. Inaddition, after the determined waiting period, the second vitals packoutput 205-2 is received and recorded by the fluid bolus log component103 at a patient vitals end entry 405. In many cases, the end command ofthe intervention command 415 will correspond to the time upon which thetarget volume of the fluid bolus is achieved, but in other cases thismay be a user-specified termination, an early termination due tocontraindication by the patient state, or a modification of the previousbolus, among other causes. Following the recording of the end of thebolus information 404 and the post-bolus vitals in the patient vitalsend entry 405, an additional set of cardiac output calculations 406 isadded to the bolus log entry 400 regarding the efficacy of the bolus forfuture reference.

The determination of efficacy of a given bolus of intravenous fluid intothe patient is now described in detail for one embodiment of the controldevice 100. First, a simple calculation is made to determine theabsolute change in cardiac output from the beginning of the bolus to theend of the bolus:

ΔCO=(CO_(end)−CO_(start))/CO_(start)  (1)

This absolute change ΔCO may not always reflect the true efficacy of afluid bolus, however. For example, if the patient 110 was losing bloodat a rapid pace, the cardiac output would steadily fall as a result. Afluid bolus given during this decline may be insufficient to actuallyovercome the blood loss and cause an increase in cardiac output, howeverit would be expected to slow the rate of decline or perhaps hold thecardiac output steady. This would still be an effective fluid bolus,though the absolute change in cardiac output would be flat or evennegative and considered ineffective by this measure. Thus, avector-based analysis can also be performed. The mean rate of change ofthe cardiac output of the patient over discrete timespans immediatelyprior to the bolus is noted and compared to the mean rate of change ofcardiac output during the period of the fluid bolus. If the rate ofchange of cardiac output becomes less negative as a result of fluid,then this is also considered an effective bolus.

An additional method of detection of possible patient conditionsconfounding calculations of efficacy of a fluid bolus is the knownrelationship between DP, CO increase with fluid, and the change in DPwith fluid.

The relationship between an increase in DP and an increase in CO inresponse to a 500 ml bolus in published and unpublished data has alinear regression coefficient (R²) of approximately 0.39 resulting inthe equation y=1.54x−1.11, where y corresponds to increase in CO and xcorresponds to increase in DP [see Cannesson M, Le Manach Y, Hofer C K,Goarin J P, Lehot J J, Vallet B, et al.; “Assessing the DiagnosticAccuracy of Pulse Pressure Variations for the Prediction of FluidResponsiveness: a ‘Gray Zone’ Approach.” Anesthesiology, 2011. 115(2):p. 231-41]. The linear regression coefficient represents the statisticalstrength of the relationship between DP and CO change. This linearregression coefficient was calculated from the clinical data set. Assuch, it's not reflected in the equation relating y and x, butrepresents the “tightness” of the equation's fit to the clinical data.

The relationship between DP and DP decrease (absolute) in response to a500 ml bolus has a linear regression coefficient (R²) of approximately0.63 for the equation y=−0.72x+4.3, where y corresponds to expecteddecrease in PPV and x corresponds to decrease in PPV.

Since the PPV/ΔPPV relationship is particularly strong, it can beexpected that for any fluid bolus the FP value can be expected todecrease predictably. If the CO does not increase as expected inresponse to a bolus and the FP does not decrease within a standarddeviation of the expected, there is a much higher likelihood that volumeloss is to blame. If the CO does increase but the FP does decrease asexpected, this is more likely to be attributable to true patient bias ordeviation from expected population-based responses.

Once these calculations are completed, the cardiac output calculations406 are stored in the bolus log entry 400 and the bolus log entry 400 isthen complete. The completed bolus log entry 400 is stored as bolus logentry 400-1 in a bolus log buffer 408. The bolus log buffer 408 includesan entire list of bolus log entries 400-1, 400-2, 400-3 through 400-Nwhich is made accessible to subsequent data processing modules(specifically the log predictor component 104 and the history analysiscomponent 105), as well as packaged for export for sharing of thesummary data.

FIG. 5 illustrates details of an embodiment of a log predictor component104 and an embodiment of a history analysis component 105 that can beused, for example, in the control device 100 of FIG. 1B. The action ofthese components is to: 1) in the case of the log predictor component104, to take bolus log entries 400 from the fluid bolus log component103 and vitals pack output data 205 from the vitals manager component101 and determine a likely resulting change in cardiac output due to anew fluid bolus given the current patient state; and 2) in the case ofthe history analysis component 105, to determine the accuracy of boththe population based predictor component 102 and the log predictorcomponent 104 in predicting the correct increase in cardiac output inprevious boluses in terms of both mean error and standard deviation ofthe mean error.

A bolus log processor component 501 receives the current vitals packoutput 205 and performs a comparison of the current patient state to thepatient state at the beginning of any previous boluses. This isaccomplished through a process referred to herein as “state similarity”that is essentially a mathematical combination of distinct hemodynamicmeasures using Bayesian weights. In one embodiment, the state similarityis the square root of the sum of the squares of the differences in eachmeasured parameter divided by the Bayesian weight of the parameter. Thespecific weights and hemodynamic measures used in this particularembodiment of the device are heart rate, systemic vascular resistance,stroke volume, and dynamic predictor, using weights of 0.33, 4, 0.2, and0.1, respectively. These weights are merely one example set and maychange, depending on patient population, new clinical observations, orthe dynamic predictor in question. The calculation of the statesimilarity for these four parameters of this embodiment is made betweenthe current state C and the previous state P as follows:

HRSS=HRC−HRP/0.33  (2)

where HRC is the current heart rate, HRP is the previous heart rate andHRSS is the heart rate state similarity;

SVRSS=SVRC−SVRP/4  (3)

where SVRC is the current systemic vascular resistance, SVRP is theprevious systemic vascular resistance and SVRSS is the systemic vascularresistance similarity state;

SVSS=SVC−SVP/0.2  (4)

where SVC is the current stroke volume, SVP is the previous strokevolume and SVSS is the stroke volume similarity state; and

DPSS=DPC−DPP/0.1  (5)

where DPC is the current dynamic predictor, DPP is the previous dynamicpredictor and DPSS is the dynamic predictor similarity state. Theresulting total state similarity measure of quality “SSTotal” iscalculated as follows:

SSTotal=100−HRSS−SVSS−SVRSS−DPSS  (6)

The state similarity parameters HRSS, SVRSS, SVSS, DPSS and SSTotal andthe corresponding bolus log entry are stored in a state similaritysubgroup buffer 502. Entries in the state similarity bolus log subgroupbuffer 502 that exhibit a quality measure SSTotal greater than a“similar” threshold level, and are of a sufficient volume and given overa timespan appropriate to draw meaningful conclusions from, are passedon to a predictive analysis component 503. Entries in the statesimilarity bolus log subgroup buffer 502 that exhibit a quality measureSSTotal greater than a “highly similar” state similarity threshold levelare given added weight. In a typical embodiment, the “similar” and“highly similar” state similarity threshold levels are 50 and 75respectively. Entries in the state similarity bolus log subgroup buffer502 that exhibit quality measures SSTotal that do not meet thesethresholds are discarded. In this way the prediction made by the logpredictor component 104 is specific to the current global hemodynamicstate of the patient 110, such that if the patient 110 changesdramatically over the course of care, previous intervention results arenot assumed to be comparable in the new state.

The log processor component 501 can start the state similarity analysisof the bolus log entries 400 with the most recent entries and workbackward in time. As previous bolus log entries 400 meet thepredetermined state similarity thresholds, a running tally of the totalstate similarity of entries found is kept. When this tally reachesanother threshold level, the log processor component 501 stops furtherstate similarity analysis and retains only the entries already captured.In this way, the algorithm manifests both a memory of its past efficacyas well as a ‘forgetfulness’, such that if some aspect of the patientchanges in a way that truly alters the patient response to fluid thesystem will not be permanently biased by the early results but ratherwill be progressively more influence by the recent results ofinterventions.

As an example of this behavior, take a patient in steady state duringsurgery. The patient cardiac output is 4.5 and the system attempts afluid bolus. The cardiac output improves to 5.6, a 21% increase. This isconsidered effective by the system and is recorded as such in one of thebolus log entries 400. Subsequently the patient has some ischemic heartinjury and the cardiac output falls back to 4.5, not because ofhypovolemia but because of a loss of inotropy. The system will likelyattempt another bolus in this state as the previous one was successful,but in this instance there will be no improvement in cardiac outputbecause the intravascular volume is already maximized. Were it not forthe forgetful component of the state similarity analysis of the boluslog entries 400, the system might continually operate using the initialsuccess indefinitely and thus continue to deliver fluid inappropriately.The emphasis of recent activity and results over more distant activityand results ensures that ineffective interventions are rapidly phasedout.

Once the entries in the state similarity bolus log subgroup buffer 502that are applicable to the current patient state are selected by the logprocessor component 501, a predictive analysis is performed on thesubgroup buffer 502 by a predictive analysis component 503. Thepredictive analysis calculates a running average of the change incardiac output recorded for the entries stored in the subgroup buffer502. In one embodiment, the entries in the subgroup buffer 502 areweighted in the running average calculation by their quality measureSSTotal as determined by the log processor component 501, such that thehigher the SSTotal of a previous bolus to the current patient state themore weight the prediction will carry in the combined running average.The output from predictive analysis component 503 is a log predictionpacket 507 and a quality rating packet 506 that includes informationabout the total number of entries in the subgroup and the correspondingstate similarity quality measures SSTotal.

Referring again to FIG. 5, the subgroup buffer 502 selected by the logprocessor component 501 is also passed into the historical accuracyanalyzer 504 of the history analysis component 105. The historicalaccuracy analyzer 504 reviews each of the selected bolus log entries 400contained in the subgroup buffer 502 and compares the cardiac outputincrease predicted by the population based predictor component 102 andby the log predictor component 104 to the actual increase seen in thepatient and calculates the average error and standard deviation of theerror for each respective predictor. This information is exported andstored in a bolus history analysis buffer 505.

FIG. 6 illustrates details of an embodiment of the prediction engine 106that can be used, for example, in the control device 100 of FIG. 1B. Theprediction engine 106 receives inputs from all of the other predictivecomponents. Specifically, the prediction engine 106 receives thepopulation prediction output 305 from the population predictor component102, the log prediction packet 507 and the quality rating packet 506from the log predictor component 104, and the bolus history analysisbuffer 505 from the history analysis component 505. The predictionengine 106 uses these inputs to generate a final combined predictedcardiac output change for the current state of the patient 110.

The process begins with the prediction engine 106 receiving a populationprediction output 305, a log prediction packet 507, a log quality rating506, and a bolus history analysis 505. The predictions included in thelog prediction packet 507 and the population prediction output 305 areadjusted by the mean error value contained in the bolus history analysisbuffer 505 for the respective components. Then the standard deviation oferror is both added and subtracted from each component to yield twopredictions for each component representing the top and bottom of therange of increase of cardiac output expected. The top and bottompredictions from a first one of the predictive components is averagedwith the top and bottom predictions from the second one of thepredictive components using the inverse of the standard deviations ofeach to weight the averages. This results in the narrower standarddeviation of the two being given more weight in the resulting value.This results in two final predictions, one for the top and one for thebottom. In the embodiment shown, a best case prediction component 602calculates the top or greatest predicted change in cardiac output and aworst case prediction component 603 calculates the bottom or leastpredicted change in cardiac output.

A final prediction component 604 combines the best case prediction andthe worst case prediction to determine the final predicted change incardiac output 605. In the embodiment shown, an “optimism” input 601 isprovided to the final prediction component 604. In this embodiment, theoptimism input 601 is a user-defined value between zero and 100 that isused to determine the final predicted change in cardiac output 605 madeby the final prediction component 604 within the best case and worstcase values determined by the best and worst case prediction components602 and 603, respectively. A value of 100 of the optimism input 601results in the best case predicted change, while a value of zero resultsin the worst case predicted change in cardiac output. Values betweenzero and 100 will result in values between the best and worst casevalues (e.g., linearly interpolated values). The final predicted changein cardiac output 605 is then output. In another embodiment, theoptimism input 601 is a fixed value of 100, 75, 50, 25, 0, or some valuein-between determined in advance or hard-coded into the algorithm. Inyet another embodiment the optimism input 601 is a range of valuesbetween the lower end of zero and the upper end of 100.

FIG. 7 illustrates details of an embodiment of an intervention decisioncomponent 107 that can be used, for example, in the control device 100of FIG. 1B. The intervention decision component 107 receives the finalpredicted change in cardiac output 605 determined by the predictionengine 106, and in conjunction with an aggression rating 710 determinesan action to be taken by the control device 100. In one embodiment theaggression rating is a user-specified value within the range −10 to +10,inclusive. In another embodiment, the aggression rating 710 can befixed, or may have a different range. Regardless of the way theaggression rating 710 is determine, the aggression rating 710 is used togenerate the intervention command 415 that is provided to the pumpmanager component 108.

The final predicted change in cardiac output 605 (given in percentageincrease in cardiac output in this embodiment) received by theintervention decision component 107 is received by an intervention rangeselector 700 and compared to decision ranges to determine theappropriate course of action. In one embodiment, there are four suchranges, though more or less may be present in other embodiments. Thelowest such range in this embodiment is the low threshold zone 704, inwhich the administration of fluid is likely to be at best not helpfuland at worst detrimental. A second range is the gray zone 703, in whichthe outcome of fluid administration is either indeterminate or mixed. Athird range is the high threshold zone 702 in which fluid is likely toincrease cardiac output significantly. A fourth range is the criticalthreshold zone 701 in which fluid is almost certain to have asubstantial positive impact. In this embodiment, the percentage increasein cardiac output for these zones are 0-7% for the low threshold zone704, 8-14% for the gray zone 703, 15-25% for the high threshold zone702, and 25% and greater for the critical threshold zone 701.

As indicated in FIG. 7, each of these zones corresponds to an action orset of actions 705, 706, 707 or 708 to be taken by the control device100. The specific response to a given zone will depend on the embodimentof the system and the number of zones. In the embodiment illustrated inFIG. 7, the response taken in each of the zones is as follows: in thelow threshold zone 704 the response is to stop any ongoing infusion andcontinue monitoring 708; in the gray zone 703, the response is tocontinue whatever action is currently taking place 707, and in somecases to deliver a test bolus (see below); in the high threshold zone702, the response is to initiate a fluid infusion, or to continue aninfusion if already running 706; and in the critical zone 701, theresponse is to accelerate any ongoing infusion to the maximum systemrate or start such an infusion if not already active 705. The finalaction is sent as an intervention command 415 to the pump manager 108.

The volumes and rates of the infusions being started upon interventionare specific to the embodiment of the control device 100 and depend onthe characteristics of the pumps 109. In an embodiment for use in ahuman patient 110, a high threshold zone 702 infusion response 706 is250 ml of fluid, given over a time span of 5-10 minutes depending on thepump capacity, and a critical zone 701 infusion 705 is 500 ml of fluidgiven over 5-10 minutes depending on the pump capacity. In otherembodiments these volumes may be modified as well as the time spans ofdelivery.

Again referring to FIG. 7 and the preferred embodiment of the device, insome cases a “test bolus” will be delivered by the pump in the Gray Zoneto determine whether or not fluid is likely to be beneficial. This testbolus can be 100 ml of fluid delivered over a time span of between oneand ten minutes. Specifically, when a patient is determined to be in thegray zone, and there are no high quality bolus log entries 400 asdetermined by the state similarity processor 501 within a specifiedtimeframe, a test bolus is initiated. In one embodiment this timeframeis two, three, four, six, ten, or twelve hours.

With further reference to FIG. 7, the aggression rating 710 is used tomodify the zone boundaries during operation of the control device 100. Apositive aggression rating 710 will lower the percentage change incardiac output of the boundaries, thereby making the control device 100more likely to deliver fluid for any given prediction by the system. Anegative aggression rating will raise the percentage change in cardiacoutput of the boundaries, and thereby make the control device 100 lesslikely to deliver fluid for any given prediction by the system. Theboundaries may, in one embodiment, be raised by a fixed value for eachpoint of the aggression rating 710 and in another embodiment may bemultiplied or divided by a value for each point of the aggression rating710. The aggression rating 710 is, in one embodiment, determined by asupervisor during operation of the control device 100.

In another embodiment, the intervention decision component 107 may alsoinclude a direct input from the vitals manager component 101 throughwhich the intervention decision component 107 receives instantaneous orshort-time-averaged vital signs. In this embodiment the interventiondecision component 107, in addition to making recommendations anddecisions about IV fluid administration also makes recommendations anddecisions about medication administrations.

In such an embodiment, vasoactive medications may be recommended to thesupervisor if deemed appropriate, or, in one embodiment, delivered tothe patient 110 directly by the system after supervisory approval orautonomously.

FIG. 8 illustrates details of an embodiment of a pump manager component108 that can be used, for example, in the control device 100 of FIG. 1B.As discussed above, the function of the pump manager component 108 to isto coordinate fluid pump activity, to monitor the fluid pumps 109 toensure proper response to commands issued to the fluid pumps 109, and tointeract with a user interface 810 when appropriate. In one embodiment,the pump manager component 108 is the only component of the controldevice 100 that receives direct instructions in the form of actionablecommands from the user interface 810.

The monitoring and control functions of the pump manager component 108are provided by interaction between a core pump manager component 801, apump director component 803, the fluid pumps 109-3 and 109-4, and a pumpmonitor 802. Any desired pump action communicated from the userinterface 810 or received in an intervention command 415 from theintervention decision component 107 is received by the core pump managercomponent 801 and relayed to the pump director component 803. Thereceived pump actions contain explicit information and formattingenabling the core pump manager component 801 to communicate thoseactions to the specific fluid pumps used in each particular embodiment.The pump director component 803 will confirm that the commands arereceived and accepted by the fluid pumps 109-3 and/or 109-4, but thepump director component 803 does not track the action of the pumps 109-3and 109-4 beyond this step. If the commands are rejected, notacknowledged, or any other communication error arises between the pumpdirector component 803 and the fluid pumps 109-3 and 109-4, this iscommunicated back to the core pump manager component 801 along with thenature of the error.

One feature of operation of the pump manager component 108 is that anycommand sent to the fluid pumps 109-1 or 109-2 for delivery of an IVfluid bolus 113-1 or 113-2 contains both a rate and a volume fordelivery. Furthermore, the fluid pumps 109 themselves can be designedsuch that only a command containing both elements is accepted by thepumps. This feature can ensure safety to the patient 110 shouldcommunication between the system and the fluid pumps fail for anyreason, and further, should the fluid pumps 109, upon loss ofcommunication with the rest of the system, fail to halt active infusionsand enter a standby mode until communication is reestablished, they willautomatically stop infusing fluid at the completion of the dictatedbolus volume. Thus, the fluid pumps 109 are not left indefinitely in aninfusing mode due to loss of system communication which could result inover-administration of fluid and pose a safety risk to the Patient.

The pump monitor component 802 is responsible for the ongoingsupervision of the fluid pumps 109 following the initial issuance of acommand by the pump director component 803. The pump monitor component802, at system specified time intervals, queries the fluid pumps 109 andrequests an update on their status. The time interval, in oneembodiment, may be 100 seconds, 10 seconds, 1 second, 1/10th seconds,1/100th seconds, or 1/1000th seconds. The status update, in oneembodiment, can contain the current pump state (on, off, waiting,infusing, alarm, error) along with detailed information about the amountof volume given in the current infusion (if active), the rate of thecurrent infusion (if active), the total volume given to this patient,the back pressure in the infusion tubing, the available volume to infuse(if present), the nature of the fluid available to infuse (if present),and detailed alarm information (if active). This information isorganized and transmitted to the core pump manager component 801.

The core pump manager component 801, as previously described, isresponsible for coordination of all pump activity involving theintervention commands 415 received from the intervention decisioncomponent 107 or from other parts of the system such as the userinterface 810. Commands are passed into the pump director component 803for transmission to the fluid pumps 109. If the intervention commands415 are accepted no further action is needed. If a command fails due toany of the communication errors discussed above, or for any otherreason, this failure is relayed back to the core pump manager component801 by the pump director component 803 for further action. If the natureof the failure may be temporary, for example, a failure ofacknowledgement of the command by one of the fluid pumps 109, the corepump manager component 801 may attempt to repeat the command one or moretimes before taking other action. In other failures that are not likelyto be resolvable by the system (for example, air detected in the pumptubing or similar failure alarms, or a pump hardware failure), then analarm condition may be set by the core pump manager component 801indicating the nature of the alarm and relayed to the user interface 810for communication with and input from the human supervisor.

In the event of an alarm condition specific to a single fluid pump 109,in an embodiment that possesses two or more fluid pumps 109, the corepump manager component 801 may continue operating and attempt tocompensate for the fluid pump 109-3 that has failed with the otheravailable fluid pump 109-4 or vice-versa until the condition can berectified by the supervisor or the condition resulting in the alarm isotherwise resolved. In the event of an alarm condition affecting theentire system, the monitoring of vital signs or the quality ofinformation received into the vitals manager component 101, or in anembodiment with only one fluid pump 109, the core pump manager component801 will attempt to halt active infusions if possible and will standbyuntil the alarm condition is resolved satisfactorily by the supervisor.Any desired actions dictated by the system in this state will beignored.

In addition to passing commands on to the pump director component 803,the core pump manager component 801 also verifies the data received fromthe pump monitor component 802 to make sure the actual state of thefluid pumps 109 is consistent with the state dictated by the lastcommand provided by the pump director component 803. If the fluid pumps109 are in a state different from the expected state but communicationbetween the fluid pumps 109 and core pump manager component 801 isintact, the core pump manager component 801 will attempt to issuecorrective commands to the pump director component 803 to place thefluid pumps 109 into the correct state. If this fails to correct thediscrepancy then a global alarm condition can be set and communicated tothe user interface 810 and further pump commands ignored until the alarmcondition is resolved.

In some instances, the state of one or more of the fluid pumps 109 willbe affected by the last command issued by the pump director component803 in an expected manner. For example, if the pump director component803 issues a command to infuse 500 ml of fluid over 10 minutes, afterthis infusion completes the fluid pumps 109 will return to standby mode.This is the expected progression of the last issued instruction and assuch will be anticipated by the core pump manager component 801 andtreated as consistent with the last issued command. Thus, no alarmcondition would result.

Referring again to FIG. 8, the core pump manager component 801 is alsoconfigured, in a decision-support or physician verification mode, forcommunication of desired actions to the user interface 810 forverification by the supervisor. In this mode, any intervention command415 received is not acted on by the core pump manager component 801autonomously. Instead, the intervention command 415 is conveyed to theuser interface 810 and displayed to the user along with the option toaccept or reject the command. If the command is rejected the core pumpmanager component 801 takes no further action and will ignore additionalidentical commands until such a time as the command changes or apre-determined time span passes. Depending on the embodiment, this timespan can be between one and sixty minutes, for example. If the commandis approved then the core pump manager component 801 passes the commandalong to the pump director component 803 for conveyance to the fluidpumps 109.

The core pump manager component 801 may also receive specific overridecommands from the supervisor. These are described in detail later.Should the core pump manager component 801 receive such an overridecommand that command is also communicated to the fluid bolus logcomponent 103 in the form of an intervention command 415, for properregistration of the intervention command as a bolus log entry 400 in thebolus log buffer 408.

Referring again to FIG. 8, in one embodiment, the pump directorcomponent 803 may be absent from the operation of the system. In thisembodiment, the system has no direct control over the fluid pumps 109whatsoever. Instead, the operation of the fluid pump(s) 109 is handledby the supervisor directly through the fluid pump(s) own controlinterface(s). The activity of the fluid pump(s) 109 is relayed back tothe core pump manager component 801 through the pump monitor component802, and this information is passed along to the fluid bolus logcomponent 103 in order to track the administration of IV Fluid 113 intothe patient 110. Meanwhile, the decisions of the control device 100 arerelayed to the supervisor through the user interface 810 asrecommendations of care. These recommendations may be displayed to thesupervisor in graphical or numeric fashion, the main point being toconvey the degree of fluid responsiveness (or predicted efficacy ofadditional fluid) in the patient 110 by the system.

FIG. 9 illustrates details of another exemplary patient-adaptivehemodynamic management system 10 in accordance with the disclosure. Inthis embodiment, an IV bag 125 of fluid is hung above the control device100 and, using purposed tubing 135 already primed and inserted into thecontrol device 100 tubing 135, channel and then connect to standard IVaccess points on the patient 110 or other tubing, delivered by thecontrol device 100 as described throughout the disclosure. Vital signsare delivered to the control device 100 through a physical connectionwith standard operating room clinical monitors 115 in the illustratedembodiment of FIG. 9. In another embodiment the clinical monitors 115are replaced with monitors integrated into the control device 100 andconnected to the patient 110, or integrated into the control device 100and in communication with wireless leads and contacts placed on thepatient 110. In another embodiment the vital signs are communicated tothe control device 100 from other monitors over a wireless network suchas Bluetooth or other wireless communication protocols. In yet anotherembodiment, the vital signs are monitored from a distance by devices notin physical contact with the patient and subsequently communicated tothe control device 100.

Again referring to FIG. 9, in the illustrated embodiment there one IVbag 125 fluid shown and one line of tubing 135 is shown connecting theIV bag 125 to the control device 100. In another embodiment, two, three,or four different IV bags 125 each with its own tubing run through adedicated channel in the device, and each IV bag 125 containingdifferent fluids may be used. In such an embodiment, the user of thecontrol device 100 may specify which bag should be used first, second,third, or so on by the system. In another embodiment of the controldevice 100, the control device 100 is made aware of what the individualfluid bags contain and will determine which fluid is appropriate basedon internal rule sets.

In another embodiment of the management system 10, one of the clinicalmonitors 115 provides a continuous or intermittent measure of patient110 hemoglobin concentrations. In another embodiment of the managementsystem 10, one of the monitors 115 provides a measure of mixed centralvenous oxygen saturation or regional central venous oxygen. In yetanother embodiment of the management system 10 one of the monitorsprovides a continuous or intermittent measure of regional or globaltissue oxygen delivery or utilization.

In another embodiment of the management system 10, the control device100 is coupled to either oxygen delivery, tissue oxygenation orutilization, blood hemoglobin concentration, mixed central venous oxygensaturation, or regional venous oxygen saturation, and one of the fluidsavailable to the control device 100 is an oxygen carrying product suchas, for example, whole blood, packed red blood cells, salvaged patientblood from the surgical field, artificial blood products or anoxygen-carrying blood substitute. In another embodiment, based on thedescribed setup, the management system 10 may elect to give such anoxygen carrying product instead of other available fluids in the eventoxygen delivery to tissues is determined to be inadequate. This mayoccur independently of decisions made regarding the benefit of fluidsalone as determined by the components described above.

FIG. 10 illustrates information flow between various components of thepatient-adaptive hemodynamic management system 10 of FIG. 1A. FIG. 10shows the same components as FIG. 1, but in this illustration theconnections between the components are not shown. Instead theconnections between the system components and the user interface 810 anda network server interface 1010 are demonstrated.

As illustrated in FIG. 10, each component of the management system 10has outputs to the user interface 810. These outputs may provide datathat can be displayed or not displayed to the supervisor depending onthe particular embodiment and implementation of the control device 100.In the embodiment shown, the only active input from the user interface810 to components of the control device 100 is an input the pump managercomponent 108, specifically the core pump manager component 801. Thisinput carries, in one embodiment, the approval or rejection ofrecommended intervention commands 415 as determined by the supervisor.Additionally, in another embodiment, this input carries supervisordirected override commands to either halt the fluid pump(s) 109 or toinitiate a new infusion. These override commands will either persistindefinitely until cleared by the supervisor or will timeout after apredetermined timespan at which point the affected fluid pump 109 willrevert to the previous mode of operation.

In one embodiment, an additional supervisor override command is a “lineflush” command. When the fluid pumps 109 are already actively infusingfluid, this line flush command has no effect. When the fluid pumps 109are in a standby state, the line flush command will cause the fluidpumps 109 to briefly activate and infuse, for example, 2, 5, 8, 10, or15 ml of fluid depending on the total volume of fluid present in the IVfluid set in the current configuration. After this volume of fluid isinfused, the fluid pumps 109 return to a standby state. The purpose ofthe line flush command is to drive the column of fluid present in theline into the patient 110 such that any medication administered into theline downstream of the fluid pumps 109 will also be flushed into thepatient 110.

An additional supervisor override command present in another embodimentof the control device 100 is a “test bolus” command. When the fluidpumps 109 are in a standby mode, the test bolus command causes a newfluid bolus to be initiated. The volume and time of this bolus are, inone embodiment, 50 ml, 100 ml, 150 ml, 200 ml, or 250 ml of fluid givenover 1, 2, 3, 4, 5, 10, or 15 minutes. If a new infusion command isreceived (and accepted by the supervisor, if necessary), during thistest bolus, the test bolus is stopped and the new bolus is started.Otherwise the test bolus will run to completion and then halt.

Referring again to FIG. 10, two components of the system interact withthe network server interface 1010, the fluid bolus log component 103 andthe population based predictor component 102. These interactions may notoccur continuously during routine operation of the system, nor are theyrequired as a component of the system.

In one embodiment, specific information is exchanged between the controldevice 100 and the network at the beginning and end of operation, or atpreset time intervals. Specifically, the bolus log 408 and the patientdemographics and comorbidities 301 are packaged and communicated toother networked components via the network server interface 1010. Thisdata includes the details of the bolus log 408, significant patientdisease states, and non-identifying demographic data such as patientage, height, weight, and gender. This data may not contain, in oneembodiment, any information making specific identification of the sourcepatient possible through the dataset.

The information communicated via the network server interface 1010 isreceived by other networked control devices 100 and processed andreorganized into population reference tables 306 which may be specificto a pertinent patient subset or to the population in general.Subsequently, at device startup time or at preset time intervals, thenetworked control devices 100 can update their own internal populationreference tables 306 through the network server interface 1010. In thisway, the population reference tables 306 will evolve over time asincreasingly detailed population data becomes available for any givenpatient population.

The network server interface 1010 may connect to other networked serversand/or control devices 100 over any of a number of forms of electroniccommunication, including but not limited to Ethernet, Wireless TCP/IP,Bluetooth, Cellular technologies, or removable media formats.

Attention is now directed to FIG. 11 which is a flowchart depicting anexemplary process 1100 of providing patient-adaptive hemodynamicmanagement in accordance with the disclosure. The process 1100 can beperformed by the patient-adaptive hemodynamic management system 10illustrated in FIGS. 1A and 1B including the control device 100illustrated in FIGS. 2-8. The process 1100 is exemplary only and notlimiting. The process 1100 can be altered, e.g., by having stages added,removed, rearranged, combined and/or performed concurrently.

The process 1100 will be described in reference to the control device100 of FIGS. 1A, 1B and FIGS. 2-8. The process 1100 begins at stage 1110where the vitals manager component 101 receives measurements of vitalsigns from clinical monitors 115. The received vital signs can includephysiologic processes such as cardiac output, stroke volume, heart rate,blood pressure and arterial pressure for example.

Upon receiving the vital signs, the process 1100 continues at stage 1114where the control device 100 determines, based at least in part upon thevital signs measurements, a predicted change in a physiologic parameterin response to a fluid bolus. The predicted change can be predicted inpart by the population based predictor component 102 based on populationstatistics, and/or in part by the log predictor component 104 and/or, inpart by the history analysis component 105 as discussed above. Inaddition, the population based prediction portion, the bolus logprediction portion and the history analysis predictions can be combinedby the prediction engine 106 as discussed above. The physiologicparameter can include one or more of cardiac output or stroke volume ofthe patient 110.

Upon predicting the change in the physiologic parameter, the process1100 continues at stage 1118 where the intervention decision component107 generates a fluid administration signal in response to the predictedchange. The administration signal can be a signal to stop a fluid boluscurrently being administered, to continue a current action and continueto test the vital signs, to start a new fluid bolus or continue a fluidbolus if one is being administered and to provide a maximum fluid bolus.

The administration signal is communicated by the intervention decisioncomponent 107 to the pump manager component 108. At stage 1122, the pumpmanager component 108 generates control signal(s) for infusion pump(s)such as the fluid pumps 108 based upon fluid administration signal tocause the fluid pumps 109 to take the appropriate action.

Attention is now directed to FIG. 12 which is a flowchart depicting aexemplary method 1200 of providing patient-adaptive hemodynamicmanagement including a user intervention in accordance with thedisclosure. The process 1200 can be performed by the patient-adaptivehemodynamic management system 10 illustrated in FIGS. 1A and 1Bincluding the control device 100 illustrated in FIGS. 2-8. The process1200 is exemplary only and not limiting. The process 1200 can bealtered, e.g., by having stages added, removed, rearranged, combinedand/or performed concurrently.

At stage 1210, the vital signs manager 101 receives measurements ofvital signs from clinical monitors 115 in a similar fashion as at stage1110 discussed above. At stage 1214, the control device 100 determines,based upon the vital signs measurements, a predicted change in aphysiologic parameter in response to a fluid bolus. Stage 1214 can beperformed in a similar fashion to stage 1114 discussed above. At stage1218, the intervention decision component 107 generates a fluidadministration signal in response to the predicted change in a similarfashion as discussed above in reference to stage 1118.

At stage 1222, instead of generating a control signal with the pumpmanager component 107, as was done at stage 1122 in the process 1100,the intervention decision component 107 causes the user interface 810 togenerate a representation of an intervention command on the userinterface which corresponds to the fluid administration signal. At stage1226, the user interface 810 receives a user input indicating acceptanceor rejection of the intervention command. If the user input is arejection, the process 1200 continues at stage 1234 where the currentinfusion or lack of infusion is continued in its existing state. If theuser input received at stage 1226 is an acceptance, the process 1200continues at stage 1230 where the user interface 810 or the interventiondecision component 107 generates a control signal or signals based uponthe intervention command received at stage 1226 and communicates thecontrol signal(s) to the pump manager component 108.

Attention is now directed to FIG. 13 which is a flowchart depictinganother exemplary process 1300 of providing patient-adaptive hemodynamicmanagement in accordance with the disclosure. The process 1300 can beperformed by the patient-adaptive hemodynamic management system 10illustrated in FIGS. 1A and 1B including the control device 100illustrated in FIGS. 2-8. The process 1300 is exemplary only and notlimiting. The process 1300 can be altered, e.g., by having stages added,removed, rearranged, combined and/or performed concurrently.

The process 1300 starts at stage 1310 where the vitals manager component101 receives information relating to an initial change in a physiologicparameter of a patient in response to administration of a first fluidbolus to the patient. At stage 1314, a predicted change in thephysiologic parameter in response to administration of a second bolus tothe patient is made by one or more of the population based predictorcomponent 102, the log predictor component 104, the history analysiscomponent 105 and the prediction engine 106 using any of the methodsdiscussed above.

Upon determining the predicted change at stage 1314, the process 1300continues to stage 1318 where the intervention decision component 107and the pump manager component 108 adjust administration of the secondfluid bolus to the patient based upon the predicted change.

Attention is now directed to FIG. 14 which is a flowchart depictinganother exemplary process 1400 for providing patient-adaptivehemodynamic management in accordance with the disclosure. The process1400 can be performed by the patient-adaptive hemodynamic managementsystem 10 illustrated in FIGS. 1A and 1B including the control device100 illustrated in FIGS. 2-8. The process 1400 is exemplary only and notlimiting. The process 1400 can be altered, e.g., by having stages added,removed, rearranged, combined and/or performed concurrently.

At stage 1410, the vitals manager component 101 receives inputinformation relating to one or more physiologic processes of a patientfrom one or more of the clinical monitors 115. At stage 1414, the fluidbolus log component 103 provides bolus log entries associated with acurrent state of the patient to the log predictor component 104 and thelog predictor component 104 determines, based at least in part upon theinput information, a subgroup of the bolus log entries. The subgroup ofentries can be biased based on a state similarity analysis as discussedabove in reference to FIG. 5.

At stage 1418, the intervention decision component 107 adjustsadministration of fluid to the patient based at least in part upon logdata included within the subgroup of bolus log entries.

Attention is now directed to FIG. 15 which is a flowchart depictinganother exemplary process 1500 for providing patient-adaptivehemodynamic management in accordance with the disclosure. The process1500 can be performed by the patient-adaptive hemodynamic managementsystem 10 illustrated in FIGS. 1A and 1B including the control device100 illustrated in FIGS. 2-8. The process 1500 is exemplary only and notlimiting. The process 1500 can be altered, e.g., by having stages added,removed, rearranged, combined and/or performed concurrently.

The process 1500 starts at stage 1514 where the vitals manager component101 determines a first effect on a physiologic parameter of the patientassociated with administration of a first fluid bolus to the patient.The vitals manager component 101 or the fluid bolus log component 103can determine the first effect by analyzing vitals signals received fromthe clinical monitors 115 and comparing the received signals topreviously received and stored signals. The physiologic parameter caninclude one or more of cardiac output, stroke volume, etc.

At stage 1518, the fluid bolus log component 103 stores firstinformation relating to the first effect in a first bolus log entry. Thefirst bolus log entry can be the bolus log entry 400 discussed above inreference to FIG. 4. The first bolos log entry can be stored in memorysuch as RAM, ROM, flash or other type of computer readable storagemedium.

At stage 1522, the vitals manager component 101 or the fluid bolus logcomponent 103 determines a second effect on the physiologic parameter ofthe patient associated with administration of a second fluid bolus tothe patient. The first effect can be determined by analyzing vitalssignals received from the clinical monitors 115.

At stage 1526, the vitals manager component 101 or the fluid bolus logcomponent 103 stores second information relating to the second effect ina second bolus log entry. The second bolus log entry can be a secondbolus log entry 400 store in a computer readable storage medium.

At stage 1530, the intervention decision component 107 generates a fluidadministration signal based at least in part upon at least one of thefirst bolus log entry and the second bolus log entry. The interventiondecision component 107 can receive information indicating one or more ofthe type of fluid bolus to administer, an amount of fluid bolus toadminister and or an rate and time over which to administer the fluidbolus from the log predictor component 104 or the prediction engine 106.

Attention is now directed to FIG. 16 which is a flowchart depictinganother exemplary process 1600 for providing patient-adaptivehemodynamic management in accordance with the disclosure. The process1600 can be performed by the patient-adaptive hemodynamic managementsystem 10 illustrated in FIGS. 1A and 1B including the control device100 illustrated in FIGS. 2-8. The process 1600 is exemplary only and notlimiting. The process 1600 can be altered, e.g., by having stages added,removed, rearranged, combined and/or performed concurrently.

The process 1600 starts at stage 1610 where the population basedpredictor component 102 receives information relating to an expectedchange in cardiac output in a population group in response toadministration of a fluid bolus. The population based information can bestored in a computer readable medium such as one of the populationreference tables 306 discussed above.

At stage 1614, the log predictor component 104 determines apatient-specific bias relating to the administration of the fluid bolusbased upon prior responses of the patient to fluid administration. Thebias can be accomplished by using a state similarity analysis of asubgroup of bolus log entries 502 as discussed above in reference toFIG. 5.

At stage 1618, the log predictor component 104 and/or the predictionengine 106 generates a predicted change in cardiac output of the patientin response to administration of the fluid bolus based at least in partupon the information relating to the expected change and thepatient-specific bias.

Attention is now directed to FIG. 17 which is a flowchart depictinganother exemplary process 1700 for providing patient-adaptivehemodynamic management in accordance with the disclosure. The process1700 can be performed by the patient-adaptive hemodynamic managementsystem 10 illustrated in FIGS. 1A and 1B including the control device100 illustrated in FIGS. 2-8. The process 1700 is exemplary only and notlimiting. The process 1700 can be altered, e.g., by having stages added,removed, rearranged, combined and/or performed concurrently.

At stage 1710, the vitals manager component 101 receives an indicationof at least one of oxygen delivery to tissues of the patient and oxygenutilization by the patient. The indication can be received from amonitoring device such as one of the clinical monitors 115 or from aninput to the user interface 810, for example. The vitals managercomponent 101 stores the indication in one of the bolus log entries inthe vitals log table 202. The vitals log entry is then communicated tothe intervention decision component 107. Alternatively, the interventiondecision component 107 can receive the indication directly.

At stage 1714, based upon the indication, the intervention decisioncomponent 107 selects an oxygen-carrying fluid from among multiplefluids capable of being infused into the patient. At stage 1718, theintervention decision component 107 signals, using a processor,selection of the oxygen-carrying fluid for infusion into the patient.

Attention is now directed to FIG. 18 which is a flowchart depictinganother exemplary process 1800 of providing patient-adaptive hemodynamicmanagement in accordance with the disclosure. The process 1800 can beperformed by the patient-adaptive hemodynamic management system 10illustrated in FIGS. 1A and 1B including the control device 100illustrated in FIGS. 2-8. The process 1800 is exemplary only and notlimiting. The process 1800 can be altered, e.g., by having stages added,removed, rearranged, combined and/or performed concurrently.

At stage 1810, a network server receives bolus log information from aplurality of fluid delivery systems communicatively coupled to thenetwork server. The fluid delivery systems can include, for example, thepatient-adaptive hemodynamic management system 10 discussed above. Afterreceiving the bolus log information, the process 1800 proceeds to stage1814 where the network server generates a set of population referencetables respectively containing portions of the bolus log informationsuch as, for example the population reference tables 306 discussed abovein reference to FIG. 3.

At stage 1818, the network server sends at least a portion of the boluslog information to one or more of the plurality of fluid deliverysystems such as the patient-adaptive hemodynamic management system 10.The patient-adaptive hemodynamic management system 10 can then utilizethe population reference tables 306 to predict changes in physiologicparameters of a patient based on an ever increasing population ofpatients.

Attention is now directed to FIG. 19 which is a flowchart depictinganother exemplary process 1900 of providing patient-adaptive hemodynamicmanagement in accordance with the disclosure. The process 1900 can beperformed by the patient-adaptive hemodynamic management system 10illustrated in FIGS. 1A and 1B including the control device 100illustrated in FIGS. 2-8. The process 1900 is exemplary only and notlimiting. The process 1900 can be altered, e.g., by having stages added,removed, rearranged, combined and/or performed concurrently.

At stage 1910, the vitals manager component 101 receive inputinformation relating to one or more physiologic processes of thepatient. The input information can be received from one of the clinicalmonitors 115 and/or from the user interface 810, for example. The vitalsmanager component 101 stores information indicative of the inputinformation in a bolus log entry of the bolus log table 202.

At stage 1914, the intervention decision component 107 determines, basedupon the input information obtained by receiving the bolus log entrycontaining the stored information, that a change has occurred in aphysiologic parameter of the patient. In response, depending on thephysiologic parameter being in one of a plurality of range of valuessuch as one of the ranges 701, 702, 703 or 704 discussed above inreference to FIG. 7, the intervention decision component 107 generates afluid administration signal based at least in part on the change in thephysiologic parameter. As discussed above, the administration signal canstop a current bolus, start a new bolus, increase or decrease aninfusion rate or maximize an infusion rate, depending on which of theplurality of threshold ranges the physiologic parameter is in.

Attention is now directed to FIG. 20 which is a flowchart depictinganother exemplary process 2000 for providing patient-adaptivehemodynamic management in accordance with the disclosure. The process2000 can be performed by the patient-adaptive hemodynamic managementsystem 10 illustrated in FIGS. 1A and 1B including the control device100 illustrated in FIGS. 2-8. The process 2000 is exemplary only and notlimiting. The process 2000 can be altered, e.g., by having stages added,removed, rearranged, combined and/or performed concurrently.

At stage 2010, the vitals manager component 101 receives inputinformation relating to one or more physiologic processes of the patient110. The input information can be received from one of the clinicalmonitors 115 or from the user interface 810, for example. Upon receivingthe input information, the vitals manager component 101 storesinformation indicative of the input information in a bolus log entry ofthe bolus log table 202.

At stage 2014, one or more of the population based predictor component102, the log predictor component 104, the history analysis component 105and the prediction engine 106 determines, based at least in part uponthe input information using the methods discussed above, a predictedchange in a physiologic parameter of the patient 110 in response toadministration of a fluid bolus to the patient.

At stage 2018, one or more of the components of the control device 100,e.g., the intervention decision component 107 or the prediction engine106, provides a fluid administration recommendation in response to thepredicted change to the user interface 810. At stage 2022, a display ofthe user interface 810 displays information relating to therecommendation on the user interface 810. At stage 2022, theintervention decision component 107 or the user interface 810 receivesuser input relating to the recommendation through the user interface810. The intervention decision component 107 or the user interface 810can then generate an administration signal to the pump manager component108 related to the recommendation.

Attention is now directed to FIG. 21 which is a flowchart depictinganother exemplary process 2100 for providing patient-adaptivehemodynamic management in accordance with the disclosure. The process2100 can be performed by the patient-adaptive hemodynamic managementsystem 10 illustrated in FIGS. 1A and 1B including the control device100 illustrated in FIGS. 2-8. The process 2100 is exemplary only and notlimiting. The process 2100 can be altered, e.g., by having stages added,removed, rearranged, combined and/or performed concurrently.

At stage 2110, the vitals manager component 101 receives informationrelating to an initial change in a physiologic parameter of a patient110 in response to administration of a first fluid bolus to the patient110. The received information can be received from one of the clinicalmonitors 115 or from the user interface 810, for example. Upon receivingthe input information, the vitals manager component 101 storesinformation indicative of the input information in a bolus log entry ofthe bolus log table 202.

At stage 2114, one or more of the population based predictor component102, the log predictor component 104, the history analysis component 105or the prediction engine 106 determines, based upon the information, apredicted change in the physiologic parameter in response toadministration of a second bolus to the patient.

At stage 2118, the prediction engine 106 or the intervention decisionengine 107 can provide a fluid administration recommendation through theuser interface 810 in response to the predicted change.

Attention is now directed to FIG. 22 which is a flowchart depictinganother exemplary process 2200 for providing patient-adaptivehemodynamic management in accordance with the disclosure. The process2200 can be performed by the patient-adaptive hemodynamic managementsystem 10 illustrated in FIGS. 1A and 1B including the control device100 illustrated in FIGS. 2-8. The process 2200 is exemplary only and notlimiting. The process 2200 can be altered, e.g., by having stages added,removed, rearranged, combined and/or performed concurrently.

At stage 2210, the vitals manager component 101 receives inputinformation relating to one or more physiologic processes of a patient.The input information can be received from one of the clinical monitors115 or from the user interface 810, for example. Upon receiving theinput information, the vitals manager component 101 stores informationindicative of the input information in a bolus log entry of the boluslog table 202.

At stage 2210, one or more of the log predictor component 104, thehistory analysis component 105 or the prediction engine 106 determines,based at least in part upon the input information, a subgroup of boluslog entries associated with a current state of the patient.

At stage 2214, the prediction engine 106 or the intervention decisionengine 107 can provide a fluid administration recommendation based atleast in part upon log data included within the subgroup of bolus logentries. The fluid administration recommendation can be provided to theuser interface 810 to prompt a user for acceptance or rejection of therecommendation. Alternatively, the fluid administration recommendationcan be converted to a signal to cause the fluid pump manager component108 to administer the recommendation automatically.

Attention is now directed to FIG. 23 which is a flowchart depicting yetanother exemplary process 2300 for providing patient-adaptivehemodynamic management in accordance with the disclosure. The process2300 can be performed by the patient-adaptive hemodynamic managementsystem 10 illustrated in FIGS. 1A and 1B including the control device100 illustrated in FIGS. 2-8. The process 2300 is exemplary only and notlimiting. The process 2300 can be altered, e.g., by having stages added,removed, rearranged, combined and/or performed concurrently.

At stage 2314, the vitals manager component 101 or the fluid bolus logcomponent 103 determines a first effect on a physiologic parameter ofthe patient associated with administration of a first fluid bolus to thepatient.

At stage 2318, the fluid bolus log component 103 stores firstinformation relating to the first effect in a first bolus log entry 400.At stage 2322, the vitals manager component 101 or the fluid bolus logcomponent 103 determines a second effect on a physiologic parameter ofthe patient associated with administration of a second fluid bolus tothe patient.

At stage 2326, the fluid bolus log component 103 stores secondinformation relating to the second effect in a second bolus log entry400.

At stage 2330, one or more of the population based predictor component102, the log predictor component 104, the history analysis component105, the intervention decision engine 107 or the prediction engine 106provides, a fluid administration recommendation based at least in partupon at least one of the first bolus log entry and the second bolus logentry using the methods discussed above. The recommendation can beprovided to the user interface 810 to prompt a user for acceptance orrejection of the recommendation. Alternatively, the fluid administrationrecommendation can be converted to a signal to cause the fluid pumpmanager component 108 to administer the recommendation automatically.

Attention is now directed to FIG. 24 which is a flowchart depictinganother exemplary process 2400 for providing patient-adaptivehemodynamic management including a clinical intervention in accordancewith the disclosure. The process 2400 can be performed by thepatient-adaptive hemodynamic management system 10 illustrated in FIGS.1A and 1B including the control device 100 illustrated in FIGS. 2-8. Theprocess 2400 is exemplary only and not limiting. The process 2400 can bealtered, e.g., by having stages added, removed, rearranged, combinedand/or performed concurrently.

At stage 2410, the log predictor component 104 receives bolus loginformation, e.g., from the fluid bolus log component 103, relating toone or more effects on a state of the patient associated with prioradministration of fluid to the patient. At stage 2414, the log predictorcomponent 104 determines, based upon the bolus log information, apredicted change in a physiologic parameter of the patient in responseto the administration of a fluid bolus to the patient.

At decision block 2418, the intervention decision component 107determines if clinical intervention is required. The decision can bemade based on the physiologic parameter lies in one of a plurality ofrange of values such as the one of the ranges 701, 702, 703 or 704discussed above in reference to FIG. 7. If it is determined thatclinical intervention is not required, the process 2400 continues tostage 2430 where the intervention decision component 107 and the pumpmanager component 108 communicate with each other to generate a fluidadministration signal based upon the predicted change.

If, at stage 2418, it is determined that clinical intervention isrequired, the process 2400 continues at stage 2422 where theintervention decision component 107 provides a fluid administrationrecommendation through the user interface 810 based upon the predictedchange. The fluid administration signal can recommend to a user whatfluid administration is recommended.

At stage 2426, the intervention decision component 107 or the userinterface 810 receives user input relating to the recommendation throughthe user interface 810. After receiving the user input, the processcontinues at stage 2430 where the intervention decision component 107 orthe user interface 810 generates a fluid administration signal to thepump manager component 108 to affect the fluid administration to thepatient based upon the predicted change and further based upon the userinput.

Attention is now directed to FIG. 25 which is a flowchart depictinganother exemplary process 2500 of providing patient-adaptive hemodynamicmanagement including a clinical intervention in accordance with thedisclosure. The process 2500 can be performed by the patient-adaptivehemodynamic management system 10 illustrated in FIGS. 1A and 1Bincluding the control device 100 illustrated in FIGS. 2-8. The process2500 is exemplary only and not limiting. The process 2500 can bealtered, e.g., by having stages added, removed, rearranged, combinedand/or performed concurrently.

At stage 2510, the population based predictor component 102 receivesinformation relating to an expected change in a physiologic parameter ofpatients in a patient population in response to infusion of a fluidbolus in the patients. At stage 2414, the population based predictorcomponent 102 determines, based at least in part upon the informationusing the methods discussed above, a predicted change in the physiologicparameter of the patient in response to administration of the fluidbolus to the patient.

At decision block 2518, the intervention decision component 107determines if clinical intervention is required. The decision can bemade based on the physiologic parameter lies in one of a plurality ofrange of values such as the one of the ranges 701, 702, 703 or 704discussed above in reference to FIG. 7. If it is determined thatclinical intervention is not required, the process 2500 continues tostage 2530 where the intervention decision component 107 and the pumpmanager component 108 communicate with each other to generate a fluidadministration signal based upon the predicted change.

If, at stage 2518, it is determined that clinical intervention isrequired, the process 2500 continues at stage 2522 where theintervention decision component 107 provides a fluid administrationrecommendation through the user interface 810 based upon the predictedchange. The fluid administration signal can recommend to a user whatfluid administration is recommended.

At stage 2526, the intervention decision component 107 or the userinterface 810 receives user input relating to the recommendation throughthe user interface 810. After receiving the user input, the processcontinues at stage 2530 where the intervention decision component 107 orthe user interface 810 generates a fluid administration signal to thepump manager component 108 to affect the fluid administration to thepatient based upon the predicted change and further based upon the userinput.

Attention is now directed to FIG. 26 which is a flowchart depicting yetanother exemplary process 2600 of providing patient-adaptive hemodynamicmanagement including a clinical intervention in accordance with thedisclosure. The process 2600 can be performed by the patient-adaptivehemodynamic management system 10 illustrated in FIGS. 1A and 1Bincluding the control device 100 illustrated in FIGS. 2-8. The process2600 is exemplary only and not limiting. The process 2600 can bealtered, e.g., by having stages added, removed, rearranged, combinedand/or performed concurrently.

At stage 2610, the fluid bolus log component 103 receives bolus loginformation relating to one or more effects on a state of the patientassociated with prior administration of fluid to the patient. At stage2614, the log predictor component 104 determines, based upon the boluslog information and using the methods discussed above, a predictedchange in a physiologic parameter of the patient in response to theadministration of a fluid bolus to the patient.

At decision block 2618, the intervention decision component 107determines if clinical intervention is required. The decision can bemade based on the physiologic parameter lies in one of a plurality ofrange of values such as the one of the ranges 701, 702, 703 or 704discussed above in reference to FIG. 7. If it is determined thatclinical intervention is not required, the process 2600 continues tostage 2630 where the intervention decision component 107 and the pumpmanager component 108 communicate with each other to generate a fluidadministration signal based upon the predicted change.

If, at stage 2618, it is determined that clinical intervention isrequired, the process 2600 continues at stage 2622 where theintervention decision component 107 provides a fluid administrationrecommendation through the user interface 810 based upon the predictedchange. The fluid administration signal can recommend to a user whatfluid administration is recommended.

At stage 2626, the intervention decision component 107 or the userinterface 810 receives user input relating to the recommendation throughthe user interface 810. After receiving the user input, the processcontinues at stage 2630 where the intervention decision component 107 orthe user interface 810 generates a fluid administration signal to thepump manager component 108 to affect the fluid administration to thepatient based upon the predicted change and further based upon the userinput.

A display of a possible user interface could comprise a standard“Starling Curve” of ventricular function, a graphical indicator of wherethe patient is perceived to be presently along that curve, and a bandshowing the ideal range of the curve for the patient. In addition,minimum and maximum curves can be displayed showing the observed rangesof cardiac function in a given patient (not shown).

FIG. 27 is a summary of a table of results from initial studies usingthe methodology of the control device 100 in simulations. Theperformance of the control device 100 was compared to the performance ofanesthesiologists across a number of dimensions. The table in FIG. 27 ispresented as evidence of the effectiveness of the methodology of thecontrol device 100 in the studies. Closed-loop management of fluidresuscitation has historically been difficult. The table in FIG. 27 isrepresentative of simulation data for a closed-loop fluid-managementalgorithm using pulse pressure variation (PPV) as the input variable.

Using a simulator which includes physiologic PPV output, twentypracticing anesthesiology residents and faculty were asked to managefluids and pressors for a one-hour simulated hemorrhage case of 2 Lblood loss over 20 minutes (group 1). One week later, they repeated thesimulation, but this time fluids were secretly managed by theclosed-loop system while practitioner fluid administrations were ignoredand only the pressors were entered (group 2). The simulation was alsorun twenty times with only the closed-loop (group 3) and twenty timeswith no management (group 4). As illustrated by the data included inFIG. 27, conditions across all groups were similar at baseline forsimulated patient weight, height, heart rate (HR), mean arterialpressure (MAP), and cardiac output (CO). Once the hemorrhage began, theclosed loop groups (2&3) intervened significantly earlier than thepractitioners (group 1) and gave more fluid. The mean and final CO washigher in both closed-loop groups than in the practitioners group, andthe coefficient of variance was lower. There was no difference in MAPbetween intervention groups, but all were significantly higher than theunmanaged group. In conclusion, the data demonstrate that closed-loopmanagement of fluid resuscitation is feasible using a dynamic parameterbased algorithm and that this approach can be used to optimize cardiacoutput.

In one or more exemplary embodiments, the functions, methods andprocesses described may be implemented in hardware, software, firmware,or any combination thereof. If implemented in software, the functionsmay be stored on or encoded as one or more instructions or code on acomputer-readable medium. Computer-readable media includes computerstorage media. Storage media may be any available media that can beaccessed by a computer. By way of example, and not limitation, suchcomputer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or otheroptical disk storage, magnetic disk storage or other magnetic storagedevices, or any other medium that can be used to carry or store desiredprogram code in the form of instructions or data structures and that canbe accessed by a computer. Disk and disc, as used herein, includescompact disc (CD), laser disc, optical disc, digital versatile disc(DVD), floppy disk and blu-ray disc where disks usually reproduce datamagnetically, while discs reproduce data optically with lasers.Combinations of the above should also be included within the scope ofcomputer-readable media.

As used herein, computer program products comprising computer-readablemedia including all forms of computer-readable medium except, to theextent that such media is deemed to be non-statutory, transitorypropagating signals.

It is understood that the specific order or hierarchy of steps or stagesin the processes and methods disclosed are examples of exemplaryapproaches. Based upon design preferences, it is understood that thespecific order or hierarchy of steps in the processes may be rearrangedwhile remaining within the scope of the present disclosure. Theaccompanying method claims present elements of the various steps in asample order, and are not meant to be limited to the specific order orhierarchy presented.

Those of skill in the art would understand that information and signalsmay be represented using any of a variety of different technologies andtechniques. For example, data, instructions, commands, information,signals, bits, symbols, and chips that may be referenced throughout theabove description may be represented by voltages, currents,electromagnetic waves, magnetic fields or particles, optical fields orparticles, or any combination thereof.

Those of skill would further appreciate that the various illustrativelogical blocks, modules, circuits, and algorithm steps described inconnection with the embodiments disclosed herein may be implemented aselectronic hardware, computer software, or combinations of both. Toclearly illustrate this interchangeability of hardware and software,various illustrative components, blocks, modules, circuits, and stepshave been described above generally in terms of their functionality.Whether such functionality is implemented as hardware or softwaredepends upon the particular application and design constraints imposedon the overall system. Skilled artisans may implement the describedfunctionality in varying ways for each particular application, but suchimplementation decisions should not be interpreted as causing adeparture from the scope of the present disclosure.

The various illustrative logical blocks, modules, and circuits describedin connection with the embodiments disclosed herein may be implementedor performed with a general purpose processor, a digital signalprocessor (DSP), an application specific integrated circuit (ASIC), afield programmable gate array (FPGA) or other programmable logic device,discrete gate or transistor logic, discrete hardware components, or anycombination thereof designed to perform the functions described herein.A general purpose processor may be a microprocessor, but in thealternative, the processor may be any conventional processor,controller, microcontroller, or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration.

The steps or stages of a method, process or algorithm described inconnection with the embodiments disclosed herein may be embodieddirectly in hardware, in a software module executed by a processor, orin a combination of the two. A software module may reside in RAM memory,flash memory, ROM memory, EPROM memory, EEPROM memory, registers, harddisk, a removable disk, a CD-ROM, or any other form of storage mediumknown in the art. An exemplary storage medium is coupled to theprocessor such the processor can read information from, and writeinformation to, the storage medium. In the alternative, the storagemedium may be integral to the processor. The processor and the storagemedium may reside in an ASIC. The ASIC may reside in a user terminal. Inthe alternative, the processor and the storage medium may reside asdiscrete components in a user terminal.

The previous description of the disclosed embodiments is provided toenable any person skilled in the art to make or use the presentdisclosure. Various modifications to these embodiments will be readilyapparent to those skilled in the art, and the generic principles definedherein may be applied to other embodiments without departing from thespirit or scope of the disclosure. Thus, the present disclosure is notintended to be limited to the embodiments shown herein but is to beaccorded the widest scope consistent with the principles and novelfeatures disclosed herein.

The disclosure is not intended to be limited to the aspects shownherein, but is to be accorded the full scope consistent with thespecification and drawings, wherein reference to an element in thesingular is not intended to mean “one and only one” unless specificallyso stated, but rather “one or more.” Unless specifically statedotherwise, the term “some” refers to one or more. A phrase referring to“at least one of” a list of items refers to any combination of thoseitems, including single members. As an example, “at least one of: a, b,or c” is intended to cover: a; b; c; a and b; a and c; b and c; and a, band c.

The previous description of the disclosed aspects is provided to enableany person skilled in the art to make or use the present disclosure.Various modifications to these aspects will be readily apparent to thoseskilled in the art, and the generic principles defined herein may beapplied to other aspects without departing from the spirit or scope ofthe disclosure. Thus, the disclosure is not intended to be limited tothe aspects shown herein but is to be accorded the widest scopeconsistent with the principles and novel features disclosed herein. Itis intended that the following claims and their equivalents define thescope of the disclosure.

1-20. (canceled)
 21. A computer-implemented method for providingclinical decision support relating to the administration of fluid to apatient, the method comprising: receiving input information relating toone or more physiologic processes of a patient; determining, based atleast in part upon the input information, a subgroup of bolus logentries associated with a current state of the patient; and providing,using the processor, a fluid administration recommendation based atleast in part upon log data included within the subgroup of bolus logentries.
 22. A computer-implemented method for providing clinicaldecision support relating to the administration of fluid to a patient,the method comprising: determining a first effect on a physiologicparameter of the patient associated with administration of a first fluidbolus to the patient; storing, using a processor, first informationrelating to the first effect in a first bolus log entry; determining asecond effect on the physiologic parameter of the patient associatedwith administration of a second fluid bolus to the patient; storing,using a processor, second information relating to the second effect in asecond bolus log entry; and providing, using the processor, a fluidadministration recommendation based at least in part upon at least oneof the first bolus log entry and the second bolus log entry.
 23. Acomputer-implemented method for providing clinical decision supportrelating to the administration of fluid to a patient, the methodcomprising: receiving bolus log information relating to one or moreeffects on a state of the patient associated with prior administrationof fluid to the patient; determining, using a processor and based uponthe bolus log information, a predicted change in a physiologic parameterof the patient in response to the administration of a fluid bolus to thepatient; and providing, using the processor, a fluid administrationrecommendation based upon the predicted change.
 24. Acomputer-implemented method for facilitating the administration of fluidto a patient, the method comprising: receiving bolus log informationrelating to one or more effects on a state of the patient associatedwith prior administration of fluid to the patient; determining, using aprocessor and based upon the bolus log information, a predicted changein a physiologic parameter of the patient in response to theadministration of a fluid bolus to the patient; and generating, usingthe processor, a fluid administration signal based upon the predictedchange.